@conference{Sanchez2011isda,
author = "Javier S{\'a}nchez-Monedero and Mariano Carbonero-Ruz and David Becerra-Alonso and Francisco Jos{\'e} Mart{\'i}nez-Estudillo and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "Ordinal classification problems are an active research area in the machine
learning community. Many previous works adapted state-of-art nominal
classifiers to improve ordinal classification so that the method can take
advantage of the ordinal structure of the dataset. However, these method
improvements often rely upon a complex mathematical basis and they usually
belong to the training algorithm and model. This paper presents a novel
method for generally adapting classification and regression models, such as
artificial neural networks or support vector machines. The ordinal
classification problem is reformulated as a regression problem by the
reconstruction of a numeric variable which represents the different ordered
class labels. Despite the simplicity and generality of the method, results
are competitive in comparison with very specific methods for ordinal
regression.",
address = "Cordoba, Spain, Spain",
booktitle = "11th International Conference on Intelligent Systems Design andApplications (ISDA 2011)",
keywords = "ordinal classification, ordinal regression, support vector machine, neural networks",
month = "nov",
pages = "1182-1187",
title = "{N}umerical variable reconstruction from ordinal categories based on probability distributions",
year = "2011",
}